{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "33278971",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>转发量</th>\n",
       "      <th>品类热度</th>\n",
       "      <th>流量推送</th>\n",
       "      <th>成交额</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2646</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>260004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>816</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>100004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1224</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>164502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1261</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>163001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1720</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>260401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1541</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>220002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>827</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>107503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>866</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>110504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1314</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>229461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1431</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>165004</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    转发量  品类热度  流量推送     成交额\n",
       "0  2646     7     5  260004\n",
       "1   816     4     6  100004\n",
       "2  1224     6     5  164502\n",
       "3  1261     6     6  163001\n",
       "4  1720     7     5  260401\n",
       "5  1541     7     5  220002\n",
       "6   827     5     7  107503\n",
       "7   866     5     9  110504\n",
       "8  1314     8     5  229461\n",
       "9  1431     6     5  165004"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 导入最基本的数据处理工具\n",
    "import pandas as pd # 导入Pandas数据处理工具包\n",
    "df_ads = pd.read_csv('直播带货.csv') # 读入数据\n",
    "df_ads.head(10) # 显示前几行数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "348b0454",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 导入数据可视化所需要的库\n",
    "import matplotlib.pyplot as plt #Matplotlib – Python画图工具库\n",
    "import seaborn as sns #Seaborn – 统计学数据可视化工具库\n",
    "# 设置字体为SimHei，以正常显示中文标签\n",
    "plt.rcParams[\"font.family\"]=['SimHei'] \n",
    "plt.rcParams['font.sans-serif']=['SimHei'] \n",
    "# 用来正常显示负号\n",
    "plt.rcParams['axes.unicode_minus']=False \n",
    "\n",
    "plt.plot(df_ads['转发量'],df_ads['成交额'],'r.', label='数据点') # 用matplotlib.pyplot的plot方法显示散点图\n",
    "plt.xlabel('转发量') # x轴Label\n",
    "plt.ylabel('成交额') # y轴Label\n",
    "plt.legend() # 显示图例\n",
    "plt.show() # 显示绘图结果！"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0f3c7bdf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>转发量</th>\n",
       "      <th>品类热度</th>\n",
       "      <th>流量推送</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2646</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>816</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1224</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1261</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1720</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    转发量  品类热度  流量推送\n",
       "0  2646     7     5\n",
       "1   816     4     6\n",
       "2  1224     6     5\n",
       "3  1261     6     6\n",
       "4  1720     7     5"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df_ads.drop(['成交额'],axis=1) # 特征集，Drop掉标签字段\n",
    "y = df_ads.成交额 # 标签集\n",
    "X.head() # 显示前几行数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f9c0da45",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    260004\n",
       "1    100004\n",
       "2    164502\n",
       "3    163001\n",
       "4    260401\n",
       "Name: 成交额, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head() # 显示前几行数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5bee2b6f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 将数据集进行80%（训练集）和20%（验证集）的分割\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, \n",
    "                                   test_size=0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "87f9ff17",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression # 导入线性回归算法模型\n",
    "model = LinearRegression() # 使用线性回归算法创建模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "46741cc2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, y_train) # 用训练集数据，训练机器，拟合函数，确定参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c5f219b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict(X_test) #预测测试集的Y值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d9d7acf4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>转发量</th>\n",
       "      <th>品类热度</th>\n",
       "      <th>流量推送</th>\n",
       "      <th>成交额真值</th>\n",
       "      <th>成交额预测值</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>529</th>\n",
       "      <td>1378</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>161003</td>\n",
       "      <td>204516.360122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>491</th>\n",
       "      <td>2090</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>207504</td>\n",
       "      <td>242936.889154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>459</th>\n",
       "      <td>2207</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>180004</td>\n",
       "      <td>249664.428319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>279</th>\n",
       "      <td>1382</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>176002</td>\n",
       "      <td>169916.207270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>655</th>\n",
       "      <td>1556</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>154003</td>\n",
       "      <td>180551.192841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>326</th>\n",
       "      <td>1574</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>232003</td>\n",
       "      <td>245577.161314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440</th>\n",
       "      <td>833</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>117001</td>\n",
       "      <td>107297.933262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1387</th>\n",
       "      <td>1797</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>213501</td>\n",
       "      <td>226089.291075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1323</th>\n",
       "      <td>804</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>119001</td>\n",
       "      <td>105630.423555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>1302</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>158002</td>\n",
       "      <td>166576.035143</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>292 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       转发量  品类热度  流量推送   成交额真值         成交额预测值\n",
       "529   1378     7     9  161003  204516.360122\n",
       "491   2090     7     5  207504  242936.889154\n",
       "459   2207     7     5  180004  249664.428319\n",
       "279   1382     6     5  176002  169916.207270\n",
       "655   1556     6     6  154003  180551.192841\n",
       "...    ...   ...   ...     ...            ...\n",
       "326   1574     8     5  232003  245577.161314\n",
       "440    833     5     7  117001  107297.933262\n",
       "1387  1797     7     5  213501  226089.291075\n",
       "1323   804     5     7  119001  105630.423555\n",
       "61    1302     6     7  158002  166576.035143\n",
       "\n",
       "[292 rows x 5 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ads_pred = X_test.copy() #测试集特征数据\n",
    "df_ads_pred['成交额真值'] = y_test #测试集标签真值\n",
    "df_ads_pred['成交额预测值'] = y_pred #测试集标签预测值\n",
    "df_ads_pred #显示数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f1338c31",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "线性回归预测集评分： 0.662399563560639\n",
      "线性回归训练集评分： 0.7293166018868376\n"
     ]
    }
   ],
   "source": [
    "print(\"线性回归预测集评分：\", model.score(X_test, y_test)) #评估模型\n",
    "print(\"线性回归训练集评分：\", model.score(X_train, y_train)) #训练集评分"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2807bab3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 分离特征和标签\n",
    "X = df_ads[['转发量']]\n",
    "y = df_ads.成交额\n",
    "\n",
    "# 划分训练集和测试集\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
    "\n",
    "# 使用线性回归模型\n",
    "model = LinearRegression()\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "# 预测\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "# 绘制预测直线\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(X_test, y_test, color='blue', label='真实值')\n",
    "plt.plot(X_test, y_pred, color='red', linewidth=2, label='预测直线')\n",
    "plt.xlabel('转发量')\n",
    "plt.ylabel('成交额')\n",
    "plt.title('转发量 vs 成交额')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
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